111 lines
3.8 KiB
Markdown
111 lines
3.8 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- mteb/raw_medrxiv
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language:
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- en
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- zh
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base_model:
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- prithivMLmods/Qwen3-1.7B-ft-bf16
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- trl
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- text-generation-inference
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- medical
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- article
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- biology
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- med
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---
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# **Canum-med-Qwen3-Reasoning (Experimental)**
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> **Canum-med-Qwen3-Reasoning** is an **experimental medical reasoning and advisory model** fine-tuned on **Qwen/Qwen3-1.7B** using the **MTEB/raw\_medrxiv** dataset.
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> It is designed to support **clinical reasoning, biomedical understanding, and structured advisory outputs**, making it a useful tool for researchers, educators, and medical professionals in experimental workflows.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Canum-med-Qwen3-Reasoning-GGUF](https://huggingface.co/prithivMLmods/Canum-med-Qwen3-Reasoning-GGUF)
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---
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## **Key Features**
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1. **Medical Reasoning Focus**
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Fine-tuned on **MTEB/raw\_medrxiv**, enabling strong performance in **biomedical literature understanding**, diagnostic reasoning, and structured medical advisory tasks.
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2. **Clinical Knowledge Extraction**
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Summarizes, interprets, and explains medical research papers, case studies, and treatment comparisons.
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3. **Step-by-Step Advisory**
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Provides structured reasoning chains for **symptom analysis, medical explanations, and advisory workflows**.
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4. **Evidence-Aware Responses**
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Optimized for scientific precision and evidence-driven output, suitable for **research assistance** and **medical tutoring**.
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5. **Structured Output Mastery**
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Capable of producing results in **LaTeX**, **Markdown**, **JSON**, and **tabular formats**, supporting integration into research and healthcare informatics systems.
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6. **Optimized for Mid-Scale Deployment**
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Balanced efficiency for **research clusters**, **academic labs**, and **edge deployments in healthcare AI prototypes**.
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---
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Canum-med-Qwen3-Reasoning"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Summarize the findings of a study on the effectiveness of mRNA vaccines for COVID-19."
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messages = [
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{"role": "system", "content": "You are a medical reasoning assistant that explains biomedical studies and provides structured clinical insights."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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---
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## **Intended Use**
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* **Medical research summarization** and literature review
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* **Diagnostic reasoning assistance** for educational or research purposes
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* **Clinical advisory explanations** in structured step-by-step format
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* **Biomedical tutoring** for students and researchers
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* **Integration into experimental healthcare AI pipelines**
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## **Limitations**
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* ⚠️ **Not a replacement for medical professionals** – should not be used for direct clinical decision-making
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* Training limited to research text corpora – may not capture rare or real-world patient-specific contexts
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* Context length limits restrict multi-document medical record analysis
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* Optimized for reasoning and structure, not empathetic or conversational dialogue |